让时间序列理解上下文语义，提升复杂决策能力。
KairosVL: Orchestrating Time Series and Semantics for Unified Reasoning
- 两阶段强化学习增强时序感知与语义推理
- 在真实与合成任务中表现优异，泛化能力显著提升
- 适合需要上下文理解的金融、医疗等时序决策场景
针对日益复杂的时序分析需求，我们提出语义条件时序推理任务，将传统纯数值建模拓展至融合上下文与语义理解。为提升模型在复杂时序问题上的推理能力，我们设计两轮强化学习框架：第一轮增强对基础时序特征的感知，第二轮聚焦语义条件下的推理。所提出的KairosVL模型在合成与真实世界任务中均取得竞争性性能。大量实验与消融研究证明，该框架不仅提升性能，还保持内在推理能力，并显著增强对未见场景的泛化能力。本工作展示了语义推理与时序建模结合的潜力，提供了一个面向现实世界时序智能的实用框架，满足紧迫的实际需求。
原文摘要 · Abstract (English)
Driven by the increasingly complex and decision-oriented demands of time series analysis, we introduce the Semantic-Conditional Time Series Reasoning task, which extends conventional time series analysis beyond purely numerical modeling to incorporate contextual and semantic understanding. To further enhance the mode's reasoning capabilities on complex time series problems, we propose a two-round reinforcement learning framework: the first round strengthens the mode's perception of fundamental temporal primitives, while the second focuses on semantic-conditioned reasoning. The resulting model, KairosVL, achieves competitive performance across both synthetic and real-world tasks. Extensive experiments and ablation studies demonstrate that our framework not only boosts performance but also preserves intrinsic reasoning ability and significantly improves generalization to unseen scenarios. To summarize, our work highlights the potential of combining semantic reasoning with temporal modeling and provides a practical framework for real-world time series intelligence, which is in urgent demand.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。